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Dynamic Graph Visualization

Dynamic graphs encode the change of relations between object over time, and as that, are a very flexible and general data encoding. Visualizing these graphs, in particular, for larger data sets and when additional information is available such as a hierarchical organization of the objects is a challenging task. Many data dimensions have to be represented at the same time:

the graph vertices (objects)

the edges induced by the graph (relations)

the weights of the edges

the inclusion edges induced by the hierarchy

the evolution of the graph over time

State-of-the-Art Report

We surveyed the field of dynamic graph visualization in a EuroVis 2014 State-of-The-Art Report (STAR). Already more than 120 papers have been published in this growing field of research, among them about 60 unique visualization techniques. We classified the techniques into a simple hierarchical taxonomy and made our literature collection also available as an interactive database.

Animated Node-Link Diagrams

Traditional approaches use a time-to-time mapping and show the time-varying graph data as animated sequences of node-link diagrams. Though this visualization strategy is very intuitive it also has some drawbacks:

if the graphs are very dense, i.e. have many edges, visual clutter occurs caused by many edge crossings

animation leads to cognitive efforts for a viewer to preserve his mental map

sophisticated layout algorithms are needed to circumvent the two former mentioned problems that have a high run time complexity

Timeline-Based Diagrams

In our research we avoid a time-to-time mapping and encode the time dimension into space instead. We use stacked graphical color coded elements to show weighted time-varying relations and we show links only implicitly by different orientations instead of direct explicit links as in node-link diagrams.

Layered TimeRadarTrees visualization showing more than 6,000,000 data points of an evolving directed and weighted graph

TimeRadarTrees visualization for soccer match results of 14 years in a part of Europe

The thumbnail view for the goalkeeper showing all weighted relations to all other players in a specific time interval

Our approach allows easily exploring a time-varying graph data set for trends, countertrends, and anomalies and has many benefits:

visual clutter is reduced by showing the links implicitly

cognitive efforts are reduced and the mental map is preserved by using static images